STUDY OBJECTIVES: To describe mathematically the relationship between patterns of sexual mixing in the general population and those of people with gonorrhoea infection, and hence to estimate the sexual mixing matrix for the general population. DESIGN: Integration of data describing sexual behaviour in the general population, with data describing sexual behaviour and mixing among individuals infected with gonorrhoea. Use of these data in a simple mathematical model of the transmission dynamics of gonorrhoea infection. SETTING: The general population of London and a genitourinary medicine (GUM) clinic in west London. PARTICIPANT: These comprised 1520 men and women living in London who were randomly selected for the national survey of sexual attitudes and lifestyles and 2414 heterosexual men and women who presented to the GUM clinic with gonorrhoea. MAIN RESULTS: The relationship between sexual mixing among people with gonorrhoea and sexual mixing in the general population is derived mathematically. An empirical estimate of the sexual mixing matrix for the general population is presented. The results provide tentative evidence that individuals with high rates of acquisition of sexual partners preferentially select other individuals with high rates as partners (assortative mixing). CONCLUSIONS: Reliable estimates of sexual mixing have been shown to be important for understanding the evolution of the epidemics of HIV infection and other sexually transmitted diseases. The possibility of estimating patterns of sexual mixing in the general population from information routinely collected in gonorrhoea contact tracing programmes is demonstrated. Furthermore, the approach we describe could, in principle, be used to estimate the same patterns of mixing, using contact tracing data for other sexually transmitted diseases, thus providing a way of validating our results.
STUDY OBJECTIVES: To describe mathematically the relationship between patterns of sexual mixing in the general population and those of people with gonorrhoea infection, and hence to estimate the sexual mixing matrix for the general population. DESIGN: Integration of data describing sexual behaviour in the general population, with data describing sexual behaviour and mixing among individuals infected with gonorrhoea. Use of these data in a simple mathematical model of the transmission dynamics of gonorrhoea infection. SETTING: The general population of London and a genitourinary medicine (GUM) clinic in west London. PARTICIPANT: These comprised 1520 men and women living in London who were randomly selected for the national survey of sexual attitudes and lifestyles and 2414 heterosexual men and women who presented to the GUM clinic with gonorrhoea. MAIN RESULTS: The relationship between sexual mixing among people with gonorrhoea and sexual mixing in the general population is derived mathematically. An empirical estimate of the sexual mixing matrix for the general population is presented. The results provide tentative evidence that individuals with high rates of acquisition of sexual partners preferentially select other individuals with high rates as partners (assortative mixing). CONCLUSIONS: Reliable estimates of sexual mixing have been shown to be important for understanding the evolution of the epidemics of HIV infection and other sexually transmitted diseases. The possibility of estimating patterns of sexual mixing in the general population from information routinely collected in gonorrhoea contact tracing programmes is demonstrated. Furthermore, the approach we describe could, in principle, be used to estimate the same patterns of mixing, using contact tracing data for other sexually transmitted diseases, thus providing a way of validating our results.
Authors: Maria Xiridou; Henrike J Vriend; Anna K Lugner; Jacco Wallinga; Johannes S Fennema; Jan M Prins; Suzanne E Geerlings; Bart J A Rijnders; Maria Prins; Henry J C de Vries; Maarten J Postma; Maaike G van Veen; Maarten F Schim van der Loeff; Marianne A B van der Sande Journal: BMC Infect Dis Date: 2013-09-18 Impact factor: 3.090